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DEA를 활용한 R&D 프로젝트의 효율성 비교 : 산업기술사업을 중심으로

Comparing Efficiencies of R&D Projects Using DEA : Focused on Industrial Technology Program

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초록

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

Abstract

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

참고문헌 (10)

  1. Banker, R., "Some models for estimating technical and scale inefficiencies in data envelopment analysis", Management Science, 30, pp.1078~1092 (1984)
  2. Banker, R., "Estimation of returns to scale using data envelopment analysis", European Journal of Operational Research, 62, pp.74~84 (1992)
  3. Charnes, A., "Measuring the efficiency of decision making units", European Journal of Operational Research, 2, pp.429~444 (1979)
  4. Fare, R., "The measurements of efficiency of production" (1985), Kluwer-Nijhoff
  5. Farris, J. A., "Evaluating the relative performance of engineering design projects : A case study using data envelopment analysis", IEEE Transactions on Engineering Management, 53, pp.471~482 (2006)
  6. Hsu, F.M., "Measuring relative efficiency of Gov.-sponsored R&D projects : A three-stage approach", Evaluation and Program Planning, 32, pp.178~186 (2009)
  7. 현만석, "DEA 모형을 이용한 공공연구기관의 기술이전 효율성 분석에 관한 연구", 산업경영시스템학회지, 31(2), pp.94~103 (2008), 한국산업경영시스템학회
  8. Mendenhall, W., "Mathematical Statistics with Applications" (1989), Duxbury
  9. 박성민, "R&D 프로젝트 효율성 상관분석 및 사업포지셔닝 조사를 위한 2단계 DEA", AR-I 성과평가모형, 2010, pp.23(6), 3285~3303[DOI]
  10. Thompson, R. G., "DEA", AR profit ratios and sensitivity of 100 large U.S. banks, 1986, pp.98, 213~229

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